Management of Information Flow and Workflow in Medical Imaging Services

ABSTRACT

Managing information flow and workflow in medical imaging services includes mapping activities in medical imaging services to a set of discrete steps in a model medical imaging process. Data concerning the medical imaging services is collected and tracked using an electronic data store and a communications network. Collected data is correlated to at least one of the discrete steps in the model medical imaging process and process metrics for performance are calculated based upon the correlated data.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/378,946, filed May 10, 2002, and titled “Management of InformationFlow and Workflow in Medical Imaging Services,” which is incorporated byreference in its entirety.

TECHNICAL FIELD

This document relates to the management of information flow and workflowin medical imaging services.

BACKGROUND

In general, diagnostic imaging services have been playing a criticalrole in detecting diseases as the first step in healthcare. The cost ofimaging services is an estimated one-third of healthcare costs per year.Diagnostic imaging service in the healthcare delivery is a complexworkflow process with many personnel and services involved. This processis an information-based transaction. Improving the workflow andinformation transformation process is a key to improving the quality andcost savings of healthcare.

SUMMARY

In one general aspect, managing information flow and workflow in medicalimaging services includes mapping activities in medical imaging servicesto a set of discrete steps in a model medical imaging process. Dataconcerning the medical imaging services is collected and tracked usingan electronic data store and a communications network. Collected data iscorrelated to at least one of the discrete steps in the model medicalimaging process and process metrics for performance are calculated basedupon the correlated data.

Implementations may include one or more of the following features. Forexample, workflow activities and/or information flow activities in themedical imaging services may be mapped to the set of discrete steps inthe model medical imaging process. Data from the workflow activitiesand/or the information flow activities in the medical imaging servicesmay be collected and tracked. In one implementation, data from theworkflow activities and/or the information flow activities of allparties involved in the medical imaging services may be collected andtracked using a communication network.

Pre-testing activities in the medical imaging services may be mapped tothe set of discrete steps in the model medical imaging process. Thepre-testing activities may include at least one of validation, tests andindications, standardization, and dissemination. Testing activities inthe medical imaging services may be mapped to the set of discrete stepsin the model medical imaging process. The testing activities may includeat least one of test ordering, reporting, access, and distribution.Post-testing activities in the medical imaging services may be mapped tothe set of discrete steps in the model medical imaging process. Thepost-testing activities may include at least one of follow-ups,adoption, and outcomes.

Process metrics may include flow metrics that correspond to one or morethe discrete steps in the model medical imaging process based on thecorrelated data. One or more limiting steps in the model medical imagingprocess may be identified using the flow metrics. The flow metrics maybe presented to a user in an order following the discrete steps in themodel medical imaging process to enable the user to identify one or morelimiting steps in the model medical imaging process.

Flow metrics may include workflow statistics that are presented to auser in an order following the discrete steps in the model medicalimaging process, where the workflow statistics correspond at least tosteps of pre-testing, testing, and post-testing. Flow metrics mayinclude information flow statistics that are presented to a user in anorder following the discrete steps in the model medical imaging process,where the information flow statistics provide a measure of efficiencyand accuracy related to the discrete steps in the model medical imagingprocess.

The process metrics also may include outcome metrics that correspond toa combination of the discrete steps in model medical imaging processbased on the correlated data. The outcome metrics may include diagnosticmetrics. The diagnostic metrics may include utilization metrics to trackan impact of using a screening test in the medical imaging services onat least one of further other non-invasive testing, invasive testing,interventional therapy, and surgery. The diagnostic metrics may includeaccuracy metrics to measure diagnostic test accuracy in terms ofpatients and specific anatomy in comparison to other invasive ornon-invasive tests. The diagnostic metrics may include clinicalcorrelation metrics to track feedback from referral physicians toresults of tests on patients of the referral physician.

The outcome metrics may include clinical outcome metrics. The clinicaloutcome metrics may include event rate metrics to track feedback relatedto clinical complications and events using a communication network. Aclinical outcome metric may include symptom metrics to track changes ina patient's symptoms. The clinical outcome metrics may include testingindex metrics to measure physiological functions of a patient as aresult of patient treatment.

The outcome metrics may include service outcome metrics. The serviceoutcome metrics may include procedure outcome metrics to track multipleprocedure utilizations over a period of time at a particular medicalimaging center as compared with benchmark targets and/or organizationalgoals. The service outcome metrics may include referral outcome metricsto track referral physician specialties and practice locations ascompared with benchmark targets and/or organizational goals.

The outcome metrics may include financial outcome metrics. The financialoutcome metrics may include reimbursement metrics to measure a billingperformance based on a reimbursement rate and speed compared to abenchmark target and/or organizational goals. The financial outcomemetrics may include inadequate reimbursement metrics to measure abilling performance based on a total number of non-reimbursed patientsand a reason for non-reimbursements.

Arrays of data and metrics may be generated to enable exporting the dataand metrics to statistical analysis computer software for furtheranalysis. A tool may be provided to input and modify medical standardsfor comparison to metrics from at least one of the discrete steps ormeasures in the model medical imaging process. The input medicalstandards may be correlated to modifiable reimbursement codes andrecommendation levels.

Analysis metrics may be generated based on an integration of differentusers of the communications network, different test and results, and theprocess metrics to enable a comprehensive analysis of different aspectsof the medical imaging services. The analysis metrics may includeutilization metrics for measuring medical imaging tests actuallyperformed compared with recommended medical imaging tests based onmedical guideline recommendations to identify under-utilization andover-utilization of particular medical imaging tests.

The analysis metrics may include referral analysis metrics foridentifying referral patterns of referral physicians in relation topatients, tests, and process and outcome metrics. The analysis metricsmay include service and marketing metrics for identifying potentialservice areas compared with modifiable benchmarks or organizationalgoals.

The analysis metrics may include clinical risk assessment metrics foruse in at least one of pre-testing risk stratification and post-testingrisk stratification.

The analysis metrics may include clinical risk assessment metrics to usean established or published model to assess risks in given patients anda need for further diagnostic imaging tests. The analysis metrics mayinclude clinical risk assessment metrics to use an established orpublished model to assess risks in given patients and compare to postimaging risk classification to assess the established or publishedmodel. The analysis metrics include clinical risk assessment metrics touse an established or published model to assess risks in given patientsand compare to patient clinical outcomes to assess the established orpublished model.

The analysis metrics may include behavior analysis metrics to evaluatebehavior patterns of at least one of referral physicians, medicalimaging centers, and patients compared to standards and outcomes in themodel medical imaging process over a period of time.

The analysis metrics may include organizational process analysis metricsto identify one or more steps in the entire process for improvement inorganizational performance for different outcomes.

The analysis metrics may include organizational process analysis metricsto examine one or more steps in the entire process to reengineer one ormore new steps or process for improvement in organizational performancefor different outcomes. The analysis metrics may include practiceprocess analysis metrics to analyze longitudinal practice from clinicalquestion, to testing, diagnosis and risk stratification,follow-up/clinical management and clinical outcomes. The analysismetrics may include test selection analysis metrics to compare differenttests. The analysis metrics may include cost effectiveness analysismetrics to compare different tests in a cost and benefit analysis.

The analysis metrics may include local practice analysis metrics for usein developing of local database system to measure and track the degreeof standard implementation, the difference of patients and practicebetween national and local, and refinement of local standardization.

The local practice analysis metrics may include metrics for use inmeasuring and tracking the degree of implementing national standards andguidelines for quality control, improve insurance reimbursement andmonitoring legal protection. The local practice analysis metrics mayinclude metrics to define the difference of patients and practicebetween national and local levels for patient population characteristicsin disease development, progress and response to treatment as well aspatient reception to new technologies and treatment. The local practiceanalysis metrics may include metrics to define the difference ofpractice between national and local level to identify the realisticlevel of local expertise to the national standards in local practicecapabilities and technology requirement. The local practice analysismetrics may include metrics to define the difference of practice betweennational and local level to identify local practice variation fromnational criteria and standards in diagnosis and diagnostic accuraciesto redefine local standards in local practice or recommend new localpractice criteria.

The analysis metrics may include outcome estimation and modification inusing indexes of an imaging test to estimate local patient outcomes,track real outcomes and refine indexes for patient outcome estimation.The analysis metrics may include outcome estimation of local patientclinical outcomes based on published landmark studies in certain localpatient populations to see the difference in estimation. The analysismetrics may include outcome estimation refinements in using a databaseto track and follow-up with patients over time to observe the realoutcomes. The analysis metrics may include outcome estimation to furtherrefine indexes for patient outcome estimation with respect to patientcharacteristics or technology.

Functions or performances of the medical imaging services may becalculated using metrics based on the correlated data. Collected dataand metrics may be extrapolated for further analysis.

These general and specific aspects may be implemented using a system, amethod, or a computer program, or any combination of systems, methods,and computer programs.

Other features will be apparent from the description and drawings, andfrom the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of a communications system for managinginformation flow and workflow in medical imaging services.

FIG. 2 is a block diagram of a computing device from the communicationssystem of FIG. 1.

FIG. 3 is a flow chart of an exemplary process for managing informationflow and workflow in medical imaging services.

FIGS. 4-21 are exemplary screen shots of a graphical user interface.

FIG. 22 is an exemplary table illustrating flow metrics calculated aspart of the exemplary process of FIG. 3.

FIG. 23 is an exemplary table illustrating an example of a filled-outtable from FIG. 22.

FIGS. 24-28 are exemplary tables illustrating different outcome metricsresulting from the process of FIG. 3.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

1. Communication Infrastructure: Personnel, Locations, Computers andNetwork Systems

For brevity, several elements in the figures described herein arerepresented as monolithic entities. However, as would be understood byone skilled in the art, these elements each may include numerousinterconnected computers and components designed to perform a set ofspecified operations and/or may be dedicated to a particular locationand/or geographical region.

Referring to FIG. 1, a communications system 100 for managinginformation flow and workflow in medical imaging services enablescommunications between multiple, different people or groups of people atdifferent locations, such as patients 110, medical practice personnel120 (e.g., registrars 121, nurses 122, and referring physicians 123),medical imaging personnel 130 (e.g., technologists 131 and imagingphysicians 132, including cardiologists, radiologists and any specialtyphysicians interpreting imaging tests), and organizations 140 (e.g.,government entities, hospital systems, insurance related entities,pharmaceutical related entities, medical equipment entities and numerousother health-care related entities). The communications are providedover a communications network 150. More specifically, for example, thecommunications system 100 enables the different people or groups ofpeople at different locations to access and exchange communications anddata over the communications network 150 with one or more electronicdata stores 160 and servers 170 and 175.

The patients 110, medical personnel 120, medical imaging personnel 130,and organizations 140 typically access the communications network 150through a computing device, such as one of computing devices 180 and185. Computing devices 180 and 185 may include, for example,general-purpose computers (e.g., personal computers), special-purposecomputers (e.g., devices specifically programmed to communicate witheach other and/or other components on the communications network 150),or a combination of one or more general-purpose computers and one ormore special-purpose computers. The computing devices 180 and 185 may bearranged to operate within or in concert with one or more other systems,such as, for example, one or more local area networks (LANs) and/or oneor more wide area networks (WANs). Other examples of computing devices180 and 185 may include a workstation, a terminal, a personal digitalassistant (PDA), other physical or virtual equipment, or somecombination thereof capable of responding to and executing instructions.Computing devices 180 and 185 may be capable of conducting peer-to-peercommunications.

Referring to FIG. 2, exemplary computing device 180 includes one or morehardware components and one or more software components. Morespecifically, computing device 180 includes various input/output (I/O)devices (e.g., mouse 1803, keyboard 1805, and display 1807) and ageneral purpose computer 1810 having a central processor unit (CPU)1820, an I/O unit 1830, memory 1840, and storage 1850 that stores dataand various programs such as operating system 1860 (e.g., DOS, Windows®,Windows® 95, Windows® 98, Windows® 2000, Windows® NT, Windows®Millennium Edition, Windows® XP, OS/2®, Macintosh OS, and Linux) and oneor more application programs 1870. Computer system 1810 also typicallyincludes some sort of communications card or device 1880 (e.g., a modemor a network adapter) for exchanging data with a communications network(e.g., communications network 150 of FIG. 1).

Examples of application programs 1870 include authoring applications(e.g., word processing programs, database programs, spreadsheetprograms, presentation programs, electronic mail programs and graphicsprograms) capable of generating documents or other electronic content,browser applications (e.g., Netscape's Navigator and Microsoft'sInternet Explorer) capable of rendering standard Internet content,personal information management (PIM) programs (e.g., Microsoft®Outlook®, Outlook® Express, and Lotus Notes®) capable of managingpersonal information, and other programs (e.g., contact managementsoftware, time management software, expense reporting applications, andfax programs).

Referring again to FIG. 1, the communications network 150 typicallyincludes a delivery network making direct or indirect communicationbetween the patients 110, medical personnel 120, medical imagingpersonnel 130, and organizations 140, irrespective of physicalseparation. Examples of a delivery network include the Internet, theWorld Wide Web, WANs, LANs, analog or digital wired and wirelesstelephone networks (e.g., public switched telephone network (PSTN),integrated services digital network (ISDN), and various types of digitalsubscriber lines (xDSL)), radio, television, cable, satellite, and/orany other delivery mechanism for carrying data.

The electronic data stores 160 includes one or more databases thatcontain electronic information related to information flow data andworkflow data in medical imaging services. In addition, differentimaging tests, their measures and results can also be input and modifiedusing standard formats and data fields. The example used here is anuclear cardiology imaging services, and the test example with gatedSPECT (single photon emission computerized tomography) imaging.

The servers 170 and 175 may include different types of servers such as aweb server and/or a database server. The servers 170 and 175 may hostthe one or more of the electronic data stores 160. The servers 170 and175 also may host a web-accessible interface that enables the patients110, medical personnel 120, medical imaging personnel 130, andorganizations 140 to exchange data with the electronic data stores 160using the communications network 150. The electronic data stores 160 andthe servers 170 and 175 enable the different people and groups of peopleto access and exchange data related to information flow and workflow inmedical imaging services simultaneously and in substantially real-time.For example, the servers 170 and 175 may host a website (e.g., a securewebsite) that is programmed to interface with the electronic data stores160. The website may be accessed by the patients 110, medical personnel120, medical imaging personnel 130, and organizations 140 throughcommunications network 150 using, for example, a browser application.

In the implementation of FIG. 1, the electronic data store 160 includesmultiple modules that perform various functions in the management ofworkflow and information flow in medical imaging services. For example,the electronic data store includes a processes/steps module 161, apersonnel/locations module 162, a performance metrics modules 163, andan analysis tools module 164. In one implementation, the differentmodules are fully integrated and interactive with each other. Theprocesses/steps module 161 and the personnel/locations module 162include the functions of the database dedicated to the workflow andinformation flow process and the specific steps of the various processesamong the different personnel in different locations. These functionsinclude presenting graphical user interfaces through a server (e.g.,servers 170 and 175) to track and monitor individual patients throughoutthe medical imaging process as the patient first visits, for example, aprimary care physician and the activities at the primary care practicethrough referral to a medical imaging center, and as the patientparticipates in follow-up care. The modules 161 and 162 providefunctionality to track a dataset for all individuals at differentlocations, their activities through the entire imaging service processand at the same time provide the functionality to accumulate andaggregate data for multiple datasets over a period of time in thedatabase, which can be used by the other modules (e.g., performancemetrics module 163 and analysis tools module 164).

The performance metrics module 163 provides the functionality formanipulating the data received to calculate various flow metrics relatedto the workflow and information flow process. These calculations includemeasurements of multiple different points in the workflow andinformation flow process that provide objective statistics that can beused to analyze the interaction of the workflow process with theinformation flow process.

The performance metrics module 163 also provides the functionality formanipulating the data received to calculate various outcome metrics,such as, for example, diagnostic testing outcome metrics, clinicaloutcome metrics, service outcome metrics, and financial outcome metrics.

The analysis tools module 164 provides the functionality to perform aseries of practical and business performance analysis, which may combinethe information from the other modules to present modifiable, flexibleand user friendly reports on various aspects of the overall process.

Each of the modules and their functionality are described in morespecific detail below.

A secure website is a website that includes some measure of accesscontrols and requires some level of authorization to interact with thewebsite. Different levels of access may be granted to different usersand the access may be segregated such that different users can onlyaccess different portions of the website and/or only may be authorizedcertain levels of access to different portions of the website, such as aread-only type access.

One or more of the computing devices 180 and 185, the electronic datastores 160, and the servers 170 and 175 may be physically located atlocations different from those illustrated in FIG. 1.

The patients 110, medical personnel 120, medical imaging personnel 130,and organizations 140 typically are located in different physicallocations from each other. For example, patients 110 may access thecommunications system 100 from their home, the medical personnel 120 mayaccess the system from physicians offices and/or hospitals, clinics orthe like, medical imaging personnel 130 may access the communicationssystem 100 from imaging centers, and organizations 140 may access thesystem from organization facilities. These people and groups of peoplemay be considered remotely-located components with respect to othercomponents of the communications system 100, such as electronic datastores 160 and servers 170 and 175, which may be consideredcentrally-located components. It is possible that a centrally-locatedcomponent may be physically located near a remotely-located componentand still maintain the same logical relationship as if the componentswere not physically located near each other. Thus, as described above,even though the different users or groups of users may be located indifferent locations, the users may access exchange data and communicatewith components and information maintained at the central location byusing a browser application that access a website that interfaces withthe electronic data store.

One or more firewalls 190 may be used to prevent unauthorized access tothe different components of the communications system 100. The firewalls190 may include firewalls that are located at the particular componentor installed on a particular component and/or may include firewalls thatare remotely located from the components and through whichcommunications must pass. The firewalls may include hardware and/orsoftware firewalls.

The network and data store systems include security administrationfunctionality such as using logon codes and different level of accesssuch that, for example, patients and referral physicians cannot modifytest results which only can be modified by testing physicians.

2. Mapping of Processes and Steps

Referring to FIG. 3, process 300 illustrates that the process is mappedas the workflow process and the corresponding information flow, andthese processes are further mapped as specific steps in medical imagingservices. Process 300 tracks both the workflow process, which includesthe interactions in who (e.g., among different users such as patients,medical personnel, medical imaging personnel, and organizations), what(e.g., activities in a step of the process), when (e.g., time andsequence of the activity), and where (e.g., locations). The informationflow process, which includes the information that may be obtained fromdifferent workflow interactions among the different user (e.g., the howand the why of the processes). Process 300 has three phases including apre-testing phase (step 310), a testing phase (step 320), and apost-testing phase (step 330). The pre-testing phase (step 310) includesthe workflow and information flow steps that generally occur prior tomedical tests being ordered and given to a particular patient. Thetesting phase (step 320) includes the workflow and information flowsteps that generally occur when a particular medical test is ordered andgiven to a particular patient. The post testing phase (step 330)includes the workflow and information flow steps that generally occurafter a particular medical test has been ordered and given to aparticular patient. Points 340 mark points in the process wheremeasurements were taken in a previous step.

In the pre-testing phase (step 310), the workflow process includesvalidation (step 311) and tests and indications (step 312). Theinformation flow steps, which parallel the pre-testing phase workflowsteps, include standardization (step 313) and dissemination (step 314).

Validation (step 311) includes information that is entered into theelectronic data store regarding medical standards, such as, for example,practice and clinical guidelines and relevant medical standards. Forexample, practice guidelines are guidelines that typically have receivedprofessional acknowledgement, peer review, and organizational approval.

Clinical practice guidelines are guidelines that typically have receivedexpert consensus and may be accepted as national and/or localguidelines. Other types of validation information include new medicaldevelopments such as evidence-based practices, results of randomizedclinical trials, and other new medical processes and technologies. Thelevel of authority, such as the American College of Cardiology, the dataof publication, the source of access, such as the name and issue of ajournal or URL, also may be entered into database.

Validation (step 311) also enables medical guideline and knowledgemanagement, such as, for example, including a guideline list;incorporating new guidelines; updating guidelines and new indications;updating/creating new guidelines in the database including name ofguidelines, organizations, publication resources and date, and a URL forlinking to the text of the guideline. Other guideline and knowledgemanagement includes the ability to update indications in a manner thatincorporates a flexible modification of both test indication with reviewof the current indication list (to see if it exists now), new indicationname, indication category (such as history, ECG, symptoms, arrhythmia,known coronary disease, MI, risk stratification, treatment andintervention), ICD code, reimbursement status with color codes; updatereimbursement status; indicated/reimbursed (red); indicated/notreimbursed (blue); not indicated/not reimbursed (black); guidelinecompliance; compliance vs. non-compliance; patient outcomes in certainpopulations; difference in local outcomes vs. national predictions; andproviding established clinical practice guidelines and indications forappropriate and inappropriate testing to primary care providers asreferral expert resources.

Tests and indications (step 312) include information relating todifferent imaging tests and procedures, risk assessment and testapplications (e.g., pre-testing patient risk assessment based onclinical information using an established model, the need for an imagingtest for further risk stratification based on standards or guidelines,such as a specific test indicated or not indicated for a specificpatient with specific medical histories, diseases, or symptoms). Testsand Indications (step 312) also includes embedding risk assessmentapplications in the program using established risk scoring algorithmsand/or models.

The information relating to validation (step 311) may be entered intothe database and used to determine particular information flow metricswhen compared against the validation related information, such as flowmetrics related to standardization (step 313). One measure ofstandardization (step 313) includes the time it takes from the date ofpublication of a medical standard until the standard is incorporatedinto routine patient care practice.

Standardization (step 313) in information flow process includes, forexample, information about the time of standards publication and thetime applied to a given patient, availability of a standard in animaging center, and the level of authority of the standard.

Dissemination (step 314) in the information flow process includes thetime of risk assessment and indication of a test applied to a patient.

In the testing phase (step 320), the workflow process includes testordering (step 321) and reporting (step 322). Test ordering (step 321)and test reporting (step 322) includes the data that may be obtainedwhen a patient visits a physician's office, a clinic, or a hospital, andthe data that is obtained from a medical imaging center when the patientis sent as a referral from the initial visit to the physician, clinic,or hospital. The information flow steps, which parallel the testingphase workflow steps, include access (step 323) and distribution (step324).

More specifically, for example, test ordering (step 321) may includepatient scheduling, prioritizing, test selection and protocol selection.

Test reporting (step 322) may include test results classification, suchas normal or abnormal, post-testing risk classification, such as low,intermediate or high risk, results delivery status, and resultsreception level, such as understand it, not understand it or morequestions.

Access (step 323) may include time of a referral physician accessimaging center for scheduling and prioritizing a patient for a test,selection of tests and protocols.

Distribution (step 324) may include time of the test results deliveredto referrals from imaging center, and the time of risk classificationfrom the patient symptom presentation.

In the post-testing phase (step 330), the workflow process includesfollow-ups (step 331) and the parallel information flow step includesadoption (step 332). The follow-ups (step 331) includes data that may beobtained following the testing period, such as data and informationobtained from follow-up visits to the primary care physician as well asdirect input from the patient.

More specifically, for example, follow-ups (step 332) may include theimpact of test results to this patient clinical management and furtherwork-up and specialty physician consultations.

Adoption (step 331) may include the time to change a patient clinicalmanagement or seeing a specialty physician based on the results.

The post-testing phase (step 330) also includes outcomes (step 350),which are the culmination of the data obtained throughout the workflowand information flow processes as the results of both flow processes tobecome outcome metrics for performance measurements. The outcome metrics(step 350) includes diagnostic outcomes (step 351), clinical outcomes(step 352), service outcomes (step 353), and financial outcomes (step354).

Diagnostic outcomes (step 351) may include feedback from patient andreferral physicians regarding the patient clinical correlations aftermaking the diagnosis from a test, test accuracy compared with otherfurther testing, and any intervention and surgery led by the test.

Clinical outcomes (step 352) may include patient symptom and changesfeedback from patients themselves and their referral physicians,functional measurements and changes from testing over time, and clinicalevents.

Service outcomes (step 353) may include service satisfactions related topersonnel and service steps in the imaging service.

Financial outcomes (step 354) may include billing and reimbursementstatus.

These measurement examples of the processes and its steps will beillustrated on Process Metrics.

3. Data Collection and Tracking

At various measurement steps of process 300, such as throughout theprocess, measurements may be taken to assist in objectively quantifyingthe workflow and information flow processes. The information obtained atthe various measurement steps may be used to calculate the processmetrics, which may provide specific information flow metrics and outcomemetrics that relate to a particular step in the process and/or toportions of the process and/or the workflow process as a whole. Thedifferent types of metrics are discussed in more detail below.

Additionally, the data that is obtained at the various steps andmeasurement points may be communicated to the central electronic datastore and server location by different users in different locations atdifferent dates in the network. The different users (e.g., patients 110,medical personnel 120, medical imaging personnel 130, and organizations140 of FIG. 1) may enter the data obtained at the various into acomputing device (e.g., computing devices 180 and 185 of FIG. 1) thatcommunicates and exchanges data with the other components of thecommunications system 100 of FIG. 1. In one exemplary implementation,the different users access the electronic data store 160 and the servers170 and 175 using a browser application to provide data input to thesecomponents by accessing a secure website that interfaces with theelectronic data store 160.

Referring to FIGS. 4-21, exemplary screen shots illustrate differentscreens of a graphical user interface presented to the different usersduring the workflow process through a browser application that allowsthe users to interface and interact with the database. Data is collectedthrough the graphical user interface and used to calculate the variousdifferent metrics.

FIG. 4 shows a screen shot 400 of a login screen and provides anindication of the links to the different systems available to differentpersonnel. FIG. 5 illustrates a screen shot 500 of a registration screenfor a new patient that enables input of patient demographic informationinto the system. For example, the demographic information may be takenand input by a registrar in a physician's office.

FIG. 6 shows a screen shot 600 of a new patient list that may bepresented to a nurse in the physician's office. The nurse may select oneof the patients from the list to access additional screens related tothat particular patient. For example, FIG. 7 illustrates a screen shot700 that shows the top part of a medical history form that may be usedto enter medical history data for a particular patient. Information thatis entered into the system is stored and may later be accessed by otherauthorized personnel. For example, the medical history information maybe taken by a nurse using the nurse information system and then savedfor accessing later by a physician using the physician informationsystem. For returning patients, the filled-out forms may be presented tothe personnel and may be updated as necessary.

FIG. 8 shows a screen shot 800 that provides a list of patients to thephysician. The physician may select one of the patients from the list toaccess additional screens related to that particular patient. Forexample, FIG. 9 illustrates a screen shot 900 that provides global riskscores based on the medical history information taken previously by thenurse and as may have been supplemented and/or updated by the physician.The electronic data store includes algorithms that take relevant patientinformation that is entered into the system and can calculate variousmedical scores based on a particular model. For example, FIG. 10 shows ascreen shot 1000 that provides a global risk scoring for a particularpatient. The data entered for the patient was applied to a risk model(e.g., the Framingham model) and the appropriate risk scores werecalculated. One benefit of the system is to provide risk scoring to thephysician at the point of care. Other risk models and algorithms may beused, and the needed information may be collected using the graphicaluser interface.

FIG. 11 illustrates a screen shot 1100 of diagnosis information that maybe entered by the physician regarding the particular patient. Forexample, the physician may evaluate the patient's presentation for otherindications of any further risk stratification using other testingprocedures that typically are referred from a practice, such as aphysician's office or hospital.

Following the entering of additional diagnosis information by thephysician, the physician may select a “Guideline” button on the userinterface. The selection of the Guideline button causes the relevantpatient information that has been entered on the previous screens to becompared against one or more known medical guidelines that have beenentered as electronic information into the electronic data store. In oneimplementation, the information from the guidelines may be entered intothe electronic data store as a logical set of queries such that theguideline information will determine which other steps should be takenin accordance with a particular guideline if a particular patientpresents with certain indications and diagnosis. Hyperlinks to the fulltext of the guidelines may be provided.

For example, FIG. 12 illustrates a screen shot 1200 of the results ofthe automatic comparison of the data collected for this particularpatient when compared to one or more guidelines. In this example, theresults of the comparison of the patient data with the guidelineindicates that a further test (e.g., a new Gated SPECT Imaging test)should be performed.

FIG. 13 illustrates a screen shot 1300 that enables the physician toorder particular types of tests that may be performed at otherlocations, such as, for example at a medical imaging center. Thephysician may order the test, which is then electronically sent to theparticular testing center (e.g., medical imaging center) for scheduling.

FIG. 14 illustrates a screen shot 1400 that is presented to a labtechnician at the testing center. In this example, a list of patientswho need testing performed is presented to the technician. Thetechnician and the medical imaging physician (e.g., nuclearcardiologist) can review the patient data entered at the physician'soffice and make an independent determination as to the type of test thatshould be performed. Any changes made at the testing center are trackedby the database and can be used in providing feedback to the referringphysician as well as providing aggregated data for use in calculatingother metrics. FIG. 15 illustrates a screen shot 1500 that enables thetesting center to modify the test as ordered by the referring physician.In this example, the type of test may be changed and the type of imagingagents may be changed. FIG. 16 illustrates a screen shot 1600 thatenables the testing center to electronically schedule the patient forthe test and FIG. 17 illustrates a screen shot 1700 that notifies thereferring physician of the scheduled test.

After the test is performed, the test results data may be entered intothe electronic data store using the graphical user interface. In oneimplementation, a lab technician may initially enter the data and themedical imaging physician can later finalize the report. The report mayinclude technical information about the quality of the study, and aninterpretation of the results of the test including any likelihood fordisease and medical events (e.g., the likelihood of coronary disease andthe risk of a cardiac event based on published long-term follow-up studyresults in similar patient populations).

FIG. 18 shows a screen shot 1800 that illustrates the test report. Thereport may include hyperlinks to different parts of the report. Again,the information is saved and stored at the electronic data store forlater access by other users in the process and for calculating differentmetrics.

FIG. 19 illustrates a screen shot 1900 that provides a listing ofpatients that include patients with test results to the referringphysician. FIG. 20 illustrates a screen shot 2000 which enables thereferring physician to provide additional follow-up data regarding thepatient and the testing results feedback, including the physician'sclinical opinion of the patient's test results. FIG. 21 illustrates ascreen shot 2100 that enables the referring physician to provide servicefeedback to the medical personnel at the testing center.

The screen shots provided in FIGS. 4-21 are exemplary and other screenshots with other information may be used. Any type of information anddata that is typically obtained as part of the medical imaging processmay be entered into the system using a graphical user interface.Examples of such information and data include, but is not limited to,patients' input of their demographics, feedback on testing results formvariety resources such as other testing centers, from self monitoringdevice, such as blood pressure and glucose level which can affect thepre-testing risk assessment scoring, clinical events, medications,patient access their own healthcare profiles, pre-test risk scoring,physician's visit, disease management tools, such as information abouthypertension and diabetes and communication tools with their physiciansand imaging centers.

The system also provide a referral physician a comprehensive patientlists for testing, scheduling, patients with results, input andmodification of patient medical history and presentations, pre-test riskassessment using embedded models, access of indications of differenttests for different patient populations, application of guidelines inputby imaging centers or organizations, ordering different tests andprotocols of a test, scheduling and prioritizing patient for tests,communication and feedback with imaging centers, specialty consultationsand organizations.

4. Performance Metrics for Assessment of Imaging Services

The data obtained during the workflow process and/or the correspondinginformation flow process can be used to calculate flow metrics thatprovide statistics of the who, what, when, where, efficiency andaccuracy (the how and why) measurements at different points in theimaging service process. The flow metrics are most useful after ameaningful number of patients have been tracked through the workflowprocess so that aggregated data may be used to calculate the flowmetrics.

Referring also to FIGS. 22 and 23, a table 2200 (FIG. 22) illustratesthe workflow statistics and the information flow metrics that may becalculated as part of process 300. Table 2300 (FIG. 23) illustrates afilled-out example of table 2200. In table 2200, patients areabbreviated as “pts.”

The data used in tables 2200 and 2300 is obtained during the workflowprocess, which tracks individual patients through the entire medicalimaging process. The data is accumulated and aggregated over time in anelectronic data store (e.g., electronic data store 160 of FIG. 1).Reference numbers corresponding to the workflow and information flowstep of FIG. 3 are also used in Table 2200 and 2300 to indicate therelationship between the steps in FIG. 3 and the metrics illustrated inthe tables.

In table 2300, a query has been run on the electronic informationcontained within the electronic data store to obtain workflow andinformation flow data on specific types of patients over a particulartime period. In this example, the data used to calculate the statisticsin the table comes from patients who presented with diabetes,hypertension, hypercholesterolemia, and were otherwise asymptomatic overthe time period from Jan. 1, 2000 to Jan. 1, 2002, which included atotal of 2,000 patients.

Table 2300 illustrates the performance of workflow process andinformation flow process, such as statistics, efficiency and accuracy inthese 2,000 patients in a organization. In the Step of Test andIndications (step 312), one can see only 25% of these group of patientsdid have risk assessment and only 11% referred to gated SPECT imaging.In the corresponding Dissemination step (step 314), it took almost 3years to reach this small magnitude of implementation of the AmericanDiabetic Association (ADA) recommendations.

However, out of the patient referred, 95% of patients actually needtests for further risk stratification after evaluation by a cardiologistin the imaging center based on guidelines. In the Test Ordering (step321) and corresponding Access steps (step 323), one can see thedifference between referral physicians' selection and actually the testsbeing done, that the accuracy rate was quite low. In the Reporting (step322) and corresponding Distribution steps (step 324), the 200 patients'imaging results and results risks were classified. However, it took toolong to delivery the results, especially in patients with abnormal scans(50%). In the Follow-up (step 331) and corresponding Adoption step (step332), post-testing risk were assessed and management plans in majoritypatients were changes. However, only 50% of abnormal patients werereferred for specialty consultations. It took over 3 years to modifymanagement on these patients.

Overview the entire workflow and information flow processes, one canidentify the limiting step(s) of the who, what, where, and when usingworkflow statistics and how and why using the information flowefficiency and accuracy. For example, one can see the critical step withthe lowest flow rate is in the Test and Indication step and the reasonis low utilization of risk assessment and not further risk stratifythose patient. The organization may start to focus on improvingunderstanding of the ADA guidelines in this patient population. As canbe seen from the example, other areas in test ordering for referralphysicians and reporting for imaging physicians need some improvement aswell.

Referring again to FIG. 3, various outcome metrics 350 may be calculatedbased on the data obtained over a period of time in the workflow andinformation flow processes. FIGS. 24-28 show exemplary tables thatdescribe the types of outcome metrics that may be calculated.

For example, FIG. 24 shows a table 2400 for various diagnostic outcomesthat may be calculated. For instance, the diagnostic outcomes includemetrics such as utilization, test accuracy, and clinical correlation.Utilization includes tracking the impact of using a screening test inmedical imaging service (e.g., usually non-invasive, such as gatedSPECT) on further invasive testing (e.g., validation of the test anddecision making for treatment, such as catheterization), interventionaltherapy (e.g., such as PTCA in patients usually with moderate diseases)and surgery (e.g., such as bypass surgery in patients usually withsevere diseases). These measurements can identify the distribution ofdisease severity in the referred patient population that have undergonethe test, the referral physician's practice pattern (behavior), theimaging center, and the organizational standardization andimplementation of risk assessment and practice guidelines in theirpractices.

For example, a referral physician who is too aggressive may send morepatients to have more invasive testing performed (e.g.,catheterization), including those patients that may not truly requiresuch an invasive test, such as those patients with normal scans. In thiscase for this particular physician, the invasive testing rate (e.g., thecath rate) in normal (n1) scans will be high. In contrast, a referralphysician who is too conservative might refer fewer patients to haveinvasive tests performed (e.g., catheterization), and the invasivetesting rate (e.g., the cath rate) in abnormal (abn1) scans may be low.

If the referred patients only have mild diseases, the use ofinterventional and surgical therapy rate will be low and, if thereferred patients have severe diseases, the rate will be high. Thecalculations reflect how the referral physicians, organization andimaging center standardize and implement risk assessment and practiceguidelines in their practices.

Accuracy includes measurements of diagnostic test accuracies in terms ofpatients, anatomy (such as location of specific coronary arteries) andcomparisons with other non-invasive tests (such as gated SPECT nucleartest vs. Echocardiography or MRI). These measures are the resultscompared to a gold standard test (such as cath to define coronarydisease) in a specific patient population using established and widelyused sensitivity and specificity, and in a given patient using Positiveor Negative Predictive Value (delete sensitivity and specificity)sensitivity and specificity that are established but not yet widely usedin practice. Although these measures are established in clinical trials(such as sensitivity of gated SPECT imaging in patients with chest painwithout history of coronary disease is about 85-90% and specificity isabout 80-85%), the measures are not readily available in clinicalpractices to calculate the accuracies with local expertise andinterpretations in their specific patient population. Also, it can helpphysicians to further identify what kind of patients and who thepatients are if the testing results are not accurate. These tools canhelp improve quality assurance.

Clinical correlation tracks the feedbacks from referral physicians tothe results of tests on their patients. Sometimes, the test results donot fit with the clinical picture, such as when a patient with chestpain had a normal gated SPECT scan. The referral physician may haveresponded with uncertainty and indicating that it is necessary to havefurther testing or consultations. Usually, medical imaging physicians donot have a systematic way to track the diagnostic outcomes if the testedpatients are not their own patients. These functions of these measures,along with feedback combined with patient clinical outcomes, serve as acritical tool for their quality improvement in interpretations, patientselection, and communication.

FIG. 25 illustrates a table 2500 for various clinical outcome metricsthat may be calculated. For example, the clinical outcome metrics thatmay be calculated include event rate metrics, symptoms metrics, andtesting indices metrics. Event rate metrics track feedback provided tothe medical imaging center by patients and referral physicians followingthe testing. For example, adverse events such as hospitalization,complications, and life threatening events (e.g., myocardialinfarction), and death may be tracked. These measures may be coordinatedwith diagnostic outcomes (as above) for quality control. Testing indexesmetrics measure a patient's physiological functions (such as leftventricular ejection fraction, a single best index for prediction ofcardiac death acknowledged by most cardiologists). These variables arecompared with prescribed treatment to see the improvement or worseningbefore and after a given treatment.

Symptoms metrics track feedback provided by patients and referralphysicians relating to the patient symptom changes (better or worse).The feedback between patients and referral physicians can be compared toidentify differences. Those patients with different responses can beflagged and identified for further investigation and follow-up.

FIG. 26 illustrates a table 2600 for various service outcome metricsthat may be calculated including, for example, procedure metrics,referral metrics, and satisfaction metrics. Procedure metrics track allprocedure utilizations in a given time period (e.g., a week, a month,and/or a year) performed at a particular imaging center, and compare theprocedures performed with benchmark and organizational goals aspotential or optimal targets. The benchmarks and organizational goalsare not fixed and may be changed by a user input to illustrate theeffect of different benchmarks and goals.

Referral metrics track referral physician specialties and their practicelocations (e.g., by town and zip code) and compare the referralsreceived at a particular medical imaging center with benchmark andorganizational goals as potential or optimal targets. Specific patientpopulations, such as patients with diabetes, hypertension, andhypercholestrolemia, can be sorted from the database to identify thesize of service coverage compared to the benchmark for serviceperformance using local government or epidemiology data ororganizational goals to determine the potentials for future service ortesting needs in a particular geographic location or from a particularreferral physician practice.

Satisfaction metrics track the feedback from referral physicians andpatients on their satisfaction with a particular imaging center servicesuch as scheduling, preparation, reporting results, and clericalaccuracy.

FIG. 27 illustrates a table 2700 for various financial outcome metricsthat may be calculated including, for example, reimbursement metrics,inadequate reimbursement metrics, cost metrics, and cost-benefitmetrics. Reimbursement metrics track a total number or percentage ofpatient for which reimbursement was received in relation to the billingcycle that can be compared to benchmarks to measure the billingperformance.

Inadequate reimbursement metrics track the number or percentage ofpatients for which no reimbursement was received, as well as the reasonsand the resources (e.g., the names of insurance companies) for theinadequate reimbursement. The follow-ups on these inadequatereimbursements may be reported for further improvement.

Cost metrics compute overhead costs from different sources in themedical imaging center or organization.

Cost-benefit metrics track the final net revenues and compare them to abenchmark for further comparison to organizational goals to assess thepotential future benefits.

FIG. 28 illustrates a table 2800 for various statistical outcomemetrics. The table can provide data in the interaction among personnel,locations, process steps and performance metrics for different purposes.The data can be extrapolated to a spreadsheet and used as input fordifferent mathematical analysis and modeling, such as statisticalanalysis models. For example, the factor analysis shown here is used toidentify which clinical variables are the best for risk assessmentpre-testing. This analysis can help an organization to use more costeffective indices to identify patients with high risk based on clinicalhistory and less expensive tests before using more expensive imagingtests.

5. Analysis Metrics

Referring again to FIG. 1, the analysis tools module 164 is used tocalculate a series of practical clinical and business performanceanalysis. These analysis tools can also help identify the who(personnel), what (tests and indexes), where (locations and process flowsteps) and when (time) in the workflow process metrics, and the how andwhy in the information flow process metrics in the process. The analysistools module 164 may include some data and metrics that may overlap withpreviously discussed metrics. However, there are a variety ofcombinations for the different metrics that may be used for differentclinical and business applications for distinct purposes. From theanalysis tools module 164, different reports may be generated that aremodifiable, flexible, and user friendly. One aspect of the analysistools module 164 includes tracking the personnel and locations in themedical imaging service. For example, tracked patient informationincludes demographics, communications (e.g., phone # and email address),insurance coverage, medical history, labs, diagnostic imaging testresults, medications, analysis from risk assessment tools, and analysisfrom feedback tools to the physician and the imaging center. Similarly,information is tracked regarding the other different users involved inthe medical imaging process, such as those described above with respectto the FIG. 1 and the personnel/locations module 162 of FIG. 1.

The analysis tools module 164 also can provide report data regardingpatient analysis such as patient selections; patient demographicsselected based on variables in the database; medical history analysisregarding certain disease trends in specific populations; presentations;symptoms and clinic/hospital visits; and tests and indexes includingtests performed in patients and specific test results with thosepatients.

The analysis tools module 164 also uses information obtained using theprocesses/steps module 161, such as the steps of workflow andinformation flow as well as related performance metrics tracked by theperformance metrics module 163 as described above with respect to theflow metrics and the outcome metrics.

The analysis tools module 164 also tracks test and indexes related tomedical imaging services, such as the various testing procedures thatcan be performed in medical imaging service. For example, in nuclearimaging, tests that may be tracked include gated SPECT, planar,first-pass radionuclide angiography (FPRNA), multiple gated radionuclideangiography (MUGA), and others. Testing results from the various teststhat may be tracked include their indexes and measurements, such aspercent of perfusion defect, left ventricular ejection fraction, andotherwise.

The analysis tools module 164 can report on utilization data such asservice market volumes, referral analysis distribution, and theutilization of certain procedures including the volume and distributionof each type of test. Other utilization information that can becalculated includes applications relative to indications (e.g.,comparison of tests actually performed with tests guideline recommendedto identify under utilization or over utilization); expanding service inunderutilized population (e.g., if tests done<recommended); identifyingpotential opportunities to increase service; comparing medicalguidelines to the standard care given for quality improvement ofservices; clinical outcome in the test for under-utilized patients tosee the risk and identify test(s) for risk stratification; admitting newpatients using new criteria of applications by organizations; avoidingsystem abuse in over-utilized population (e.g., if testsdone>recommended); identifying current over-utilized tests and patientpopulations; evaluating cost effectiveness of a given technology and itsapplication on certain patient populations; and recommending avoidanceof over testing in specific test and patient populations using newcriteria of applications by organizations.

The analysis tools module 164 also can provide report data regardingreferral analysis, such as referral analysis distribution; patientselections; referral physicians' specialty, demographics and geography;new leads analysis of referrals (large geographic areas and zip codes);patient service populations and patients with specific diseasepopulations; referral volume and testing procedures; test selections andperformance compared their selection with test actually done byspecialty imaging physicians; and projected referrals and potentialreferrals.

The analysis tools module 164 also can provide service and marketinganalysis, such as customer service outcomes; customer service clinicoutcomes; referral analysis distribution; service map and marketcapture; potential service areas compared to benchmark or organizationalgoals (projections); reimbursement: current volume and revenue,projected volume and revenue; referrals in different areas; andepidemiology and patient distributions.

The analysis tools module 164 also provides customer service andrelationship management including the supply chain analysis such as, forexample, customer service outcomes; customer service clinic outcomes;referral analysis distribution; feedback: clinical monitoring andoutcomes; service satisfaction feedback; and the feedback differencesbetween patients and referral physicians.

The analysis tools module 164 also provides clinical risk assessment,integration and management analysis including, for example, patientselection based on risk class; applications in utilization for riskassessment, high risk assessment and test indications; risk assessmentand management for pre-testing and post-testing; and pre-testing riskstratification that uses clinical variables to quantify risks (belowaverage risk, moderate above average risk and high risk compared to thegeneral population) in certain patients (such as diabetes andhypertension) for clinical outcomes (such as myocardial infarction ordeath) based on established models (e.g., Framingham model). Other riskassessment analysis includes, for example, a risk classificationalgorithm that is built in to estimate a given patient's relative riskof developing a disease (such as coronary heart disease), absolute riskof the disease consequences or complications (such as heart attacks) andabsolute risk of death (such as cardiac death) in the future (such as 10years). The application of pre-test risk stratification will give thereferral physician an opportunity to evaluate for the need of furtherdiagnostic testing using new technology for better risk stratificationin individual patients. The high risk category based on a patient'sclinical history may serve as a “red flag” for further riskstratification using a diagnostic testing procedure.

Still other risk assessment analysis includes post-testing riskstratification. A diagnostic test has more accurate estimation of agiven patient based on the patient physiology and functions to furtherassess the risk of the patient into low, intermediate or high risk ofcardiac events in the near future (such as the cardiac death ormyocardial infarction in 1-2 years). This will serve as a triage toolfor clinical management. For example, when a patient with pre-testinghigh risk has a test with a low risk results, this patient most likelywill undergo conservative medical therapy and observation. On the otherhand, if a patient with pre-testing above average risk has a test withintermediate to high risk results, this patient most likely will undergoaggressive invasive investigation (such as catheterization) andinterventional therapy (such as angioplasty or bypass surgery).

Still other risk assessment analysis includes risk comparison;evaluation of a served patient population for utilization; a list ofpatients that compares pre-testing risks with post testing risks;comparison of risks with clinical outcomes; outcomes analysis includingdatabase analysis of clinical and diagnostic outcomes in certainpatients population to assess the predictive accuracy of pre-test modeland testing technology; and redefining a new model and technologyapplication to identify problems in the application of pre-test modeland testing technology on a certain patient population to betterredefine the model and use of the technology or applications.

The analysis tools module 164 also enables behavior analysis such as theorganizational process of individual physicians; referral analysisdistribution; customer service outcomes; customer service clinicoutcomes; referral and practice patterns in certain patient populations;patient outcomes using different strategies; evaluation of behavior (orpractice) pattern in individual patients, referral physicians andimaging centers comparing their behavior (adoption of standardguidelines and activities of execution) in the processes of workflow andinformation flow with the outcomes over time; and using information flowand workflow metrics to measure behavior and monitor behavior change.

The analysis tools module 164 also enables factor analysis such asoutcome focused analysis to determine the impact of clinical risks andtest indices and to select appropriate cutoff points between populationsample size and severity of indices.

The analysis tool module 164 enable local practice analysis metrics foruse in developing of local database system to measure and track thedegree of standard implementation, the difference of patients andpractice between national and local, and modification of localstandardization. It can be used in measuring and tracking the degree ofimplementing national standards and guidelines for quality control,improve insurance reimbursement and monitoring legal protection. Theanalysis tool also can define the difference of patients and practicebetween national and local levels for patient population characteristicsin disease development, progress and response to treatment as well aspatient reception to new technologies and treatment. The analysis toolalso can define the difference of practice between national and locallevel to identify the realistic level of local expertise to the nationalstandards in local practice capabilities and technology requirement. Theanalysis tool also define the difference of practice between nationaland local level to identify local practice variation from nationalcriteria and standards in diagnosis and diagnostic accuracies toredefine local standards in local practice or recommend new localpractice criteria.

The analysis tools module 164 also enables organizational processanalysis such as organizational process analysis in individualphysicians; workflow and information flow analysis; identification oflimiting step, personnel and service; profile top/bottom forresponsibilities and actions for improvements; objective performancequantifications and comparisons; quality assurance for medical imaginglabs; performance analysis in comparison with benchmarks; simulation andprediction of modified process outcomes with new organizational goals;and dissemination of new policies/guideline/process through theorganization.

Furthermore, the analysis tools module 164 enables cycle analysis, suchas analysis of historical data and trends using feedback and outcome tomodify steps in the processes; monitoring and enhancing current processor redefining and reengineering new process; clinical cycle management(e.g., clinical questions, testing, diagnosis, follow-up/management andclinical outcomes); revenue cycle management (test indications,ordering, performance, reporting, follow-up and billing, financialoutcomes); cost effective cycle analysis (comparing different testingapproaches and outcomes in a specific patient population); andsimulation of outcomes comparing with benchmarks or organizationalprojected goals with modification of current steps of processes.

The described systems, methods, and techniques may be implemented indigital and/or analog electronic circuitry, computer hardware, firmware,software, or in combinations of these elements. Apparatus embodyingthese techniques may include appropriate input and output devices, acomputer processor, and a computer program product tangibly embodied ina machine-readable storage device for execution by a programmableprocessor. A process embodying these techniques may be performed by aprogrammable processor executing a program of instructions to performdesired functions by operating on input data and generating appropriateoutput. The techniques may be implemented in one or more computerprograms that are executable on a programmable system including at leastone programmable processor coupled to receive data and instructionsfrom, and to transmit data and instructions to, a data storage system,at least one input device, and at least one output device. Each computerprogram may be implemented in a high-level procedural or object-orientedprogramming language, or in assembly or machine language if desired; andin any case, the language may be a compiled or interpreted language.Suitable processors include, by way of example, both general and specialpurpose microprocessors. Generally, a processor will receiveinstructions and data from a read-only memory and/or a random accessmemory. Storage devices suitable for tangibly embodying computer programinstructions and data include all forms of non-volatile memory,including by way of example semiconductor memory devices, such asErasable Programmable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), and flash memory devices;magnetic disks such as internal hard disks and removable disks;magneto-optical disks; and Compact Disc Read-Only Memory (CD-ROM). Anyof the foregoing may be supplemented by, or incorporated in,specially-designed ASICs (application-specific integrated circuits).

It will be understood that various modifications may be made withoutdeparting from the spirit and scope of the claims. For example,advantageous results still could be achieved if steps of the disclosedtechniques were performed in a different order and/or if components inthe disclosed systems were combined in a different manner and/orreplaced or supplemented by other components. Accordingly, otherimplementations are within the scope of the following claims.

For example, the above-described methods, systems, and computer programsmay be applied to all medical imaging services, even though some of theexamples provided above are with respect to nuclear cardiology.

1-20. (canceled) 21-55. (canceled)
 56. A computer implemented method forquantitatively assessing and managing the performance of a workflowprocess and an information flow process of a medical procedure serviceprocess, the method comprising: creating, by the computer, a modelincluding activities related to the workflow process and informationflow process of the medical procedure service process, the workflowprocess and information flow process of the medical procedure serviceprocess including a pre-procedure phase, a procedure phase, and apost-procedure phase; wherein the pre-procedure phase includes discretesteps that correspond to validation activities from the workflow processwith standardization activities from the information flow process, andprocedure and indication activities from the workflow process withdissemination activities from the information flow process; wherein theprocedure phase includes discrete steps that correspond to orderingactivities from the workflow process with access activities from theinformation flow process, and reporting activities from the workflowprocess with distribution activities from the information flow process;wherein the post-procedure phase includes discrete steps that correspondto follow-up activities from the workflow process with adoptionactivities from the information flow process; mapping, by the computer,the activities of the medical procedure service process model to thecorresponding discrete steps of the workflow process and informationflow process of the pre-procedure phase, procedure phase, andpost-procedure phase; receiving, by the computer, data generated for theactivities performed during the discrete steps of each phase of theworkflow process and information flow process by a plurality of patientsparticipating in the medical procedure service process; receiving, bythe computer, one or more known medical procedure guidelines forcomparison with the received patient-related data generated duringperformance of the activities related to the workflow process andinformation flow process of the medical procedure service process;calculating, by the computer, statistically based workflow metrics forthe entire medical procedure service process model using the receiveddata generated during performance of the mapped activities, wherein theworkflow metrics are calculated as a ratio of the total number of aparticular occurrence per the total number of results in the sample orthe time taken to arrive at a particular occurrence, the workflowmetrics including validation activities, indication activities, orderingactivities, reporting activities, and follow-up activities; calculating,by the computer, statistically based information flow metrics for theentire medical procedure service process model using the received datagenerated during performance of the mapped activities, wherein theinformation flow metrics are calculated as a ratio of the total numberof a particular occurrence per the total number of results in the sampleor the time taken to arrive at a particular occurrence, the informationflow metrics including, in terms of efficiency and accuracy,standardization activities, dissemination activities, access activities,distribution activities, and adoption activities; and generating, by thecomputer, a user interface including a performance analysis of theactivities in the mapped phases of the workflow process and informationflow process based on one or more of the workflow metrics, informationflow metrics, and received data generated in the performance of themapped activities.
 57. The method of claim 56 further comprising:determining, by the computer, outcome metrics for the plurality ofpatients using the received data generated in the performance of themapped activities, the outcome metrics including one or more ofdiagnostic outcomes, clinical outcomes, service outcomes, and financialoutcomes; wherein the diagnostic outcome metrics summarize at leastutilization, test accuracy, and clinical correlation related of themedical procedure guideline; wherein the clinical outcome metricssummarize at least event rates, symptoms, and testing indexes related tothe medical procedure guideline; wherein the service outcome metricssummarize at least procedures, referrals, and satisfaction related tothe medical procedure guideline; wherein the financial outcome metricssummarize at least reimbursed totals and averages, inadequatereimbursement, costs, and cost-benefits related to the medical imagingservice guideline.
 58. The method of claim 57 further wherein saidgenerating step generates a user interface including a performanceanalysis of the activities in the mapped phases of the workflow processand information flow process based on one or more of the workflowmetrics, information flow metrics, outcome metrics, and received datagenerated in the performance of the mapped activities.
 59. The computerimplemented method of claim 56 further comprising: determining, by thecomputer, procedure utilization including at least one ofunderutilization and over utilization.
 60. The computer implementedmethod of claim 59 wherein said generating step generates a userinterface including a performance analysis of the activities in themapped phases of the workflow process and information flow process basedon one or more of the workflow metrics, information flow metrics,procedure utilization, outcome metrics, and received data generated inthe performance of the mapped activities.
 61. The computer implementedmethod of claim 60 wherein the performance analysis indicates efficiencyof at least one of procedure utilization, outcome metrics, workflowmetrics and information flow metrics.
 62. The computer implementedmethod of claim 60 wherein the performance analysis indicates accuracyof at least one of procedure utilization, outcome metrics, workflowmetrics and information flow metrics compared to a procedure guideline.63. The computer implemented method of claim 56 further comprisingdetermining, by the computer, referral metrics including at least one ofreferral source, procedure volume, performance of procedure referralcompared to procedure guidelines, and performance of potential procedurereferral as compared to procedure guidelines.
 64. The computerimplemented method of claim 56 further comprising generating analysismetrics based on an integration of different users of the communicationsnetwork, different tests and results, and the process metrics to enablea comprehensive analysis of different aspects of the medical procedureservice process.
 65. The computer implemented method of claim 64 whereinthe analysis metrics include local practice analysis metrics and furthercomprising developing of local database system to measure and track thedegree of standard implementation, the difference of patients andpractice between national and local populations, and refinement of localstandardization.
 66. The method as in claim 65 wherein the localpractice analysis metrics include metrics for use in measuring andtracking the degree of implementing national standards and guidelinesfor quality control, improve insurance reimbursement and monitoringlegal protection.
 67. The method as in claim 65 wherein the localpractice analysis metrics include metrics to define the difference ofpatients and practice between national and local levels for patientpopulation characteristics in disease development, progress and responseto treatment as well as patient reception to new technologies andtreatment.
 68. The method as in claim 65 wherein the local practiceanalysis metrics include metrics to define the difference of practicebetween national and local level to identify local practice variationfrom national criteria and standards in diagnosis and diagnosticaccuracies to redefine local standards in local practice or recommendnew local practice criteria.
 69. The computer implemented method ofclaim 56 further comprising: determining, by the computer, referralanalysis metrics and further comprising identifying referral patterns ofreferral physicians in relation to patients, tests, referral analysismetrics and process and outcome metrics.
 70. The computer implementedmethod of claim 56 further comprising determining, by the computer,behavior analysis metrics and wherein said performance analysisevaluates behavior patterns of at least one of referral physicians,medical procedure providers, and patients compared to standards andoutcomes in the model medical procedure service process over a period oftime.
 71. The computer implemented method of claim 56 further comprisingdetermining organizational process analysis metrics and wherein saidperformance analysis identifies one or more steps in the entire processfor improvement in organizational performance for different outcomesusing the organizational process analysis metrics.